DSSM. The Deep Structured Semantic Model [1]} maps text strings to a common semantic space with a deep neural network (DNN) that converts high-dimensional text vectors to a dense representation. Its first layer applies a letter n-gram based word hashing as a linear transformation to reduce the dimensionality of feature vectors and to increase the model's robustness against out-of-vocabulary inputs. Its final layer computes the cosine similarity between the embedding vectors as a measure of their relevance. This model is trained on clickthrough data to maximize the conditional likelihood of a clicked document given the query.
